Lexical Semantics and Ontologies
Tutorial at the ACL/HCSnet 2006 Advanced Program in
Natural Language Processing
Paul Buitelaar
Language Technology Lab &
Competence Center Semantic Web
DFKI GmbH
Saarbrücken, Germany
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Overview


Day 1: Words and Meanings

Human language as a system

How do words relate to each other
Day 2: Words and Object Descriptions

Human language as a means of representation

How do words represent objects in the/a world
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Day 1 - Introduction

Words and Meanings

Synsets and Senses


Related Senses


Lexical Semantics in WordNet
Generative Lexicon and CoreLex
Domains and Senses

Tuning WordNet to a Domain
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Meanings
Lexical Semantics in WordNet
Generative Lexicon and CoreLex
Tuning WordNet to a Domain
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
WordNet

Lexical Semantic Resource

Semantic Lexicon


Lexical Database


Maps words to meanings (senses)
Machine readable (has a formal structure)
Freely available

http://wordnet.princeton.edu/
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
WordNet - Origins
In 1985 a group of psychologists and linguists at Princeton University
undertook to develop a lexical database …
The initial idea was to provide an aid to use in searching dictionaries
conceptually, rather than merely alphabetically …
WordNet … instantiates hypotheses based on results of psycholinguistic
research …
… expose such hypotheses to the full range of the common vocabulary
In anomic aphasia, there is a specific inability to name objects. When
confronted with an apple, say, patients may be unable to utter ‘‘apple,’’ even
though they will reject such suggestions as shoe or banana, and will
recognize that apple is correct when it is provided. (Caramazza/Berndt 1978)
Miller, George A., Richard Beckwith, Christiane Fellbaum, Derek Gross and
Katherine J. Miller. ``Introduction to WordNet: an on-line lexical database.'' In:
International Journal of Lexicography 3 (4), 1990, pp. 235 - 244.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Synsets

WordNet is organized around word meaning (not
word forms as with traditional lexicons)



Word meaning is represented by “synsets”
Synset is a “Set of Synonyms”
Example

{board, plank}


Piece of lumber
{board, committee}

Group of people
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Synset Hierarchy

Synsets are organized in hierarchies

Defines:



generalization (hypernymy)
specialization (hyponymy)
Example
{entity}
…
{whole, unit}
{building material}
{lumber, timber}
{board, plank}
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
hypernymy
hyponymy
Hierarchies (WordNet 1.7)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Hierarchy Example (WordNet 2.1)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Synsets and Senses

Synsets represent word meaning


Words that occur in several synsets have a
corresponding number of meanings (senses)
Example
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
WordNet 2.1
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
(Other) WordNet Relations

Synonymy


Hypernymy/Hyponymy


Similar in meaning
Generalization and Specialization
Meronymy

Part-of


e.g. study, bathroom, ... meronym house
Antonymy

Opposite in meaning

e.g. warm
antonym cold
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Meanings
Lexical Semantics in WordNet
Generative Lexicon and CoreLex
Tuning WordNet to a Domain
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Systematic Polysemy

Homonymy

bank
embankment
institution

We walked along the bank of the Charles river.
Did he have an account at the HBU bank?
Systematic Polysemy

school
group (of people)
(learning) process
organization
building
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
The school went for an outing.
School starts at 8.30
The school was founded in 1910.
The school has a new roof.
Semantic or Pragmatic?
Semantic Analysis
Lexical Items
of the
Language
school
Obj1
Objects
in the
World
Obj2
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Pragmatic Analysis
school
Obj4
Obj3
Obj1
Obj2
Obj4
Obj3
Underspecified Discourse Referents

Anaphora Resolution

[A long book heavily weighted with military technicalities]NP:event-physical_objectcontent
, in this edition it is neither so long event nor so technical content as it was
originally.

Metonymy


The Boston office called

office > person

person part-of office
Bridging

Peter bought a car. The engine runs well.


engine part-of car
The Boston office called. They asked for a new price.

office > person
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Generative Lexicon Theory
Type Coercion
I began the book
book > event
event ‘has-relation-with’ book
read is-a event

multifaceted representation of lexical semantics

reflecting systematic / regular / logical polysemy
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Generative Lexicon Theory
Qualia Structure (Pustejovsky 1995)
Formal
book
inheritance (is-a / hyponymy)
Constitutive
book
modification (part-of / meronymy)
constitutive
read, …
telic
causality („how did the object come about“)
Agentive
book
section, …
purpose („what is the object used for“)
Telic
book
artifact, communication, …
formal
agentive
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
write, …
CoreLex (Buitelaar 1998)

Automatic Qualia Structure Acquisition



CoreLex is an attempt to automatically acquire underspecified
lexical semantic representations that reflect systematic polysemy
These representations can be viewed as shallow Qualia
Structures
Sense Distribution in WordNet

Systematic polysemy can be empirically studied in WordNet by
observing sense distributions
>> If more than two words share the same sense distribution (i.e.
have the same set of senses), then this may indicate a pattern of
systematic polysemy (adapted from Apresjan 1973)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Systematic Polysemous Classes
book
1.{publication}
2.{product, production}
3.{fact}
4.{dramatic_composition, dramatic_work}
5.{record}
6.{section, subdivision}
7.{journal}
=> artifact
=> artifact
=> communication
=> communication
=> communication
=> communication
=> artifact
Systematic Polysemous Class
“artifact communication”
amulet annals armband arrow article ballad bauble beacon bible birdcall blank blinker boilerplate
book bunk cachet canto catalog catalogue chart chevron clout compact compendium convertible
copperplate copy cordon corker ... guillotine homophony horoscope indicator journal laurels lay
ledger loophole marker memorial nonsense novel obbligato obelisk obligato overture pamphlet
pastoral paternoster pedal pennant phrase platform portrait prescription print puzzle radiogram rasp
recap riddle rondeau … statement stave stripe talisman taw text tocsin token transcription trophy
trumpery wand well whistle wire wrapper yardstick
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
From WordNet to CoreLex
Noun1
Nounn
Basic Type1
Basic Type1
Systematic
Polysemous
Class1
Systematic
Polysemous
Classn
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Other Examples
“animal natural_object”
alligator broadtail chamois ermine lapin leopard muskrat ...
“natural_object plant”
algarroba almond anise baneberry butternut candlenut cardamon ...
“action artifact group_social”
artillery assembly band church concourse dance gathering institution ...
“action attribute event psychological”
appearance concentration decision deviation difference impulse outrage …
“possession quantity_definite”
cent centime dividend gross penny real shilling
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
CoreLex vs. WordNet
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Representation and Interpretation

„Dotted Types“ (Pustejovsky)


Lexical types are either simple (human, artifact, ...) or
complex (information AND physical_object)
Can be represented with a „dotted type“, e.g.
informationphysical_object

In (Cooper 2005) interpreted as a record type (a delicious lunch
can take forever):
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Related Work

Apresjan 1973


Nunberg & Zaenen 1992


Semi-productive polysemy and sense extension.
Peters, Peters & Vossen 1998


Word Sense Ambiguation: Clustering Related Senses.
Copestake & Briscoe 1996


Systematic polysemy in lexicology and lexicography.
Bill Dolan 1994


Regular Polysemy.
Automatic Sense Clustering in EuroWordNet.
Tomuro 1998

Semi-Automatic Induction of Systematic Polysemy from WordNet.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Meanings
Lexical Semantics in WordNet
Generative Lexicon and CoreLex
Tuning WordNet to a Domain
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Reducing Ambiguity

WordNet has too many senses …

Reduce Ambiguity

Cluster related senses (CoreLex)

Tune WordNet to an application domain
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Domains and Senses
Domains determine Sense Selection, e.g.



English: cell

prison cell in the Politics/Law domain

living cell in the Biomedical domain
English: tissue

living tissue in the Biomedical domain

cloth in the Fashion domain
German: Probe

test in the Biomedical domain

rehearsal in the Theater domain
>> Compute Domain-Specific Sense
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Approaches

Subject Codes


Topic Signatures


Domain codes are in the dictionary
Compute (domain-specific) context models from dictionary
definitions, domain corpora, web resources
Tuning of WordNet to a domain



Top Down: Cucchiarelli & Velardi, 1998
Bottom Up: Buitelaar & Sacaleanu, 2001
Related recent work: McCarthy et al, 2004; Chan & Ng, 2005;
Mohammad & Hirst, 2006
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Subject Codes


Subject Codes (as used in LDOCE) indicate a
domain in which a word is used in a particular sense
Examples (2600 codes)

Sub-Field Codes


MDZP (Medicine:Physiology)
Code Combinations


MLCO (Meteorology+Building) e.g. lightning conductor
MLUF (Meteorology+Europe+France) e.g. Mistral
SN (sounds)
high
DG (drugs)
ML (meteorology)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Adding Subject Codes to WordNet

Grouping Synsets together across POS
MEDICINE

Nouns:
Verbs:
doctor#1, hospital#1
operate#7
Grouping Synsets together across Sub-Hierarchies
SPORT
life_form#1: athlete#1
physical_object#1: game_equipment#1
act#2 : sport#1
location#1 : playing_field#1
Magnini B. & Cavaglià G. Integrating Subject Field Codes into WordNet In: Proceedings LREC 2000
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
WordNet DOMAINS
Sense
WordNet synset and gloss
Domains
1
Depository, financial institution, bank, banking concern, banking company (a financial institution)
Economy
2
Bank (sloping land)
Geography, Geology
3
Bank (a supply or stock held in reserve)
Economy
4
Bank, bank building (a building)
Architecture, Economy
5
Bank (an arrangement of similar objects)
Factotum
6
Savings bank, coin bank, money box, bank (a container)
Economy
7
Bank (a long ridge or pile)
Geography, Geology
8
Bank (the funds held by a gambling house )
Economy, Play
9
Bank, cant, camber (a slope in the turn of a road)
Architecture
10
Bank (a flight maneuver.)
Transport
Bernardo Magnini, Carlo Strapparava, Giovanni Pezzuli, and Alfio Gliozzo. Using domain information for
word sense disambiguation. In: Proceedings of the SENSEVAL2 workshop 2001.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
WSD with Subject Codes

Match between set of words in the context of the ambiguous
word and the set of words (“neighborhoods”) in the definitions
+ sample sentences of all senses that share a Subject Code
bank: Economics
bank: Medicine and Biology
write
safe
sum
medicine
product hold
account
person
put
origin
place
take
money
order
treatment blood
keep
pay
supply
use
store
paper
draw
cheque
organ
comb
human
hospital
Guthrie J. A. & Guthrie I. & Wilks Y. & Aidinejad H. Subject Dependent Co-Occurrence and Word Sense
Disambiguation In: Proceedings of ACL 1991.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Topic Signatures from the Web

Construct Topic Signatures for WordNet synsets/senses

Retrieve document collections from the web and use queries
constructed for each WordNet sense, e.g.
( boy
AND ( altar boy OR ball boy OR … OR male person )
AND NOT (man OR … OR broth of a boy OR
son OR … OR mama’s boy OR black ) )
Agirre E. & Ansa O. & Hovy E. & Martinez D. Enriching very large ontologies using the WWW In: Proc. of
the Ontology Learning Workshop ECAI 2000
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Top Down Tuning – Cucchiarelli & Velardi

Automatically find the best set of (WordNet)
senses that:



“… represent at best the semantics of the domain”
“[has the] … ‘right’ level of abstraction, so as to
mediate between over-ambiguity and generality”
“… [is] balanced …, i.e. words should be evenly
distributed among categories”
Alessandro Cucchiarelli, Paola Velardi Finding a domain-appropriate sense inventory for semantically
tagging a corpus. Natural Language Engineering 4/4, p.325-344, Dec. 1998.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Methods Used

Create alternative sets of balanced categories by use of an adapted
version of the Hearst/Schütze algorithm

Apply a scoring function to find the best set, with parameters:

Generality


Discrimination Power


Different senses lead to different categories
(Domain) Coverage


Highest possible level of generalization with a small number of categories is
preferred
Words in the domain corpus that are represented by the selected categories
Average Ambiguity

Ambiguity reduction is measured by the inverse of the average ambiguity of
all words
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Balanced Categories - Hearst/Schütze



Reduce WordNet noun hierarchy to a set of 726 disjoint categories,
each consisting of a relatively large number of synsets and of an
average size, with as small a variance as possible
Group categories together into a set of 106 super-categories
according to mutual co-occurrence in a training corpus
Measure the frequency of categories on domain corpora
12.200
legal_system, ...
26.459
religion, ...
11.782
government, ...
25.062
breads, ...
7.859
politics, ...
24.356
mythology, ...
United States Constitution
Genesis
Hearst M. & Schütze H. Customizing a Lexicon to Better Suit a Computational Task In: Proceedings ACL
SIGLEX Workshop 1993
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Generality
Generality of Category Set Ci: 1/DM(Ci)
Average Distance between the Categories of Ci and the topmost synsets.
4+3/2
3/1
1 n
DM (Ci )  *  dm(cij )
n j 1
Ci = {Ci1, Ci2}
DM (Ci )= (3.5 + 3) / 2 = 3.25
Topmost SynSet
Ci1
Ci2
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
General SynSet
Discrimination Power
Discrimination Power of Category Set Ci:
(Nc(Ci) - Npc(Ci))/ Nc(Ci)
where Nc(Ci) is the number of words that reach at least one category of Ci and
Npc(Ci) is the number of words that have at least two senses that reach the
same category cij of Ci
Ci1
Ci2
Ci3
Ci4
Ci = {Ci1 Ci2 Ci3 Ci4}
General Synset
Sense
w1
w2
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
w3
Domain Word
Coverage & Average Ambiguity
Coverage of Category Set Ci: Nc(Ci)/W
where Nc(Ci) is the number of words that reach at least one category in Ci
Inverse of Average Ambiguity of Category Set Ci: 1/A(Ci)
1
A(Ci )
N c (Ci)
N c (C i )
*  Cwj(C i )
j 1
where Nc(Ci) is the number of words that reach at least one category in Ci , and
for each word w in this set, Cwj(Ci) is the number of categories in Ci reached
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Best Category Set (WSJ)
Category
Higher-level synset
C1
person, individual, someone, mortal, human, soul
C2
instrumentality, instrumentation
C3
written communication, written language
C4
message, content, subject matter, substance
C5
measure, quantity, amount, quantum
C6
action
C7
activity
C8
group action
C9
organization
C10
psychological feature
C11
possession
C12
state
C13
location
Top Down categories for the financial domain, based on the Wall Street Journal
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Sense Selection with WSJ Set
Sense
Synset hierarchy for sense
Top synset for sense
1
capital > asset
possession (C11)
2
support > device
instrumentality (C2)
4
document > writing
written communication (C3)
5
accumulation > asset
possession (C11)
6
ancestor > relative
person (C1)
Senses for stock - kept by domain tuning on the Wall Street Journal
Sense
Synset hierarchy for sense
3
stock, inventory > merchandise, wares >…
7
broth, stock > soup > …
8
stock, caudex > stalk, stem > …
9
stock > plant part > …
10
stock, gillyflower > flower > …
11
malcolm stock, stock > flower …
12
lineage, line of descent > … > genealogy > …
14
lumber, timber > …
Senses for stock - discarded by domain tuning on the Wall Street Journal
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Bottom Up Tuning – Buitelaar & Sacaleanu

Ranking of WordNet synsets according to a
domain-specific corpus

Compute term relevance against reference corpus

Compute synset relevance according to term
relevance (where term = synonym in synset)

Ranking can be used in WSD (similar to usage of
‘most frequent heuristic’)
Paul Buitelaar, Bogdan Sacaleanu Ranking and Selecting Synsets by Domain Relevance In:
Proceedings of WordNet and Other Lexical Resources: Applications, Extensions and Customizations,
NAACL 2001 Workshop, June 3/4 2001
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
TFIDF
N
tfidf ( w)  tf . log(
)
df ( w)
The word is more important if it appears
several times in a target document
The word is more important if it
appears in less documents
tf(w)
term frequency (number of word occurrences in a document)
df(w)
document frequency (number of documents containing the word)
N
number of all documents
tfIdf(w)
relative importance of the word in the document
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Term and Synset Relevance

Term Relevance

Relevance Score of Synset Members
rlv (t | d )  log( tft , d ) log(
N
)
dft
where t represents the term, d the domain, N is the total number of domains

Synset Relevance

Cumulated Relevance Score for a Synset
rlv (c | d )   rlv (t | d )
tc
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Extended Synset Relevance

Lexical Coverage

Take Length of the Synset Into Account
[Gefängniszelle, Zelle] ("prison cell")
[Zelle]
("living cell")

rlv (c | d )  
tc
T
rlv (t | d )
c
Hyponyms

Take Hyponyms Into Account
[Zelle,Gefängniszelle,Todeszelle]
[Zelle,Körperzelle,Pflanzenzelle]
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
T
rlv (c | d )   rlv (t | d )
tc  c
Experiment – Medical Domain
Rank ed Terms -- with English translation(s)
Ranked Concepts
yes
Eingriff (operation, intervention)
all
Infek tion (infection)
1.
2.
1.
2.
[Eingriff:c, Operation:c, Abtreibung, Biopsie, ...]
[Eingreifen:c, Eingriff:c, Intervention:c]
[Entzündung:c, Infektion:c, Infektionskrankheit:c, ...]
[Ansteckung:c, Infektion:c, Übertragung:c]
all
Studie (study, report)
all
Prophylaxe (prophylaxis)
yes
Gewebe (tissue)
all
Medizin (medicine)
yes
Gefäß (vascular, container)
yes
Zelle (cell)
all
Einschränk ung (constraint, restriction)
all
Aufnahme (intak e, reception)
yes
Sek tion (section)
all
Ausdehnung (spread, dimensions)
yes
Geburt (birth, rebirth)
yes
Abweichung (abnormality, divergence)
yes
Probe (test, rehearsal)
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
1.
2.
[Experiment:c, Studie:c, Test:c, Versuch:c,...]
[Abhandlung:c, Studie:c]
[Prophylaxe:c, Empfängnisverhütung, Impfung, Verhütung]
[Prophylaxe:c, Vorbeugung:c, Vorsorge:c, ...]
[Gewebe:c, Körpergewebe:c, Bindegewebe, Tumor, ...]
[Gewebe:c, Kleiderstoff:c, Stoff:c, Textilstoff:c, ...]
[Medizin:c, Chirurgie, Frauenheilkunde, Gynäkologie, ...]
[Arznei:c, Arzneimittel:c, Heilmittel:c, Medikament:c, ...]
[Gefäß:c, Blutgefäß, Haargefäß, Herzkranzgefäß, Lymphgefäß]
[Gefäß:c, Container, Form, Pokal, Schale, Schüssel, Tonne, ...]
[Zelle:c, Körperzelle, Pflanzenzelle]
[Gefängniszelle:c, Zelle:c, Todeszelle]
[Beschränkung:c, Einschränkung:c, Vorbehalt:c]
[Beschränkung:c, Degression:c, Drosselung:c, Einschränkung:c]
[Aufnahme:c, Aufzeichnung:c, Mitschnitt:c, Protokoll, ...]
[Aufnahme:c, Beherbergung:c, Unterbringung:c, Notaufnahme, ...]
[Autopsie:c, Leichenöffnung:c, Obduktion:c, Sektion:c]
[Amtsbereich:c, Dezernat:c, Geschäftsbereich:c, Sektion:c, ...]
[Ausdehnung:c, Rauminhalt:c, Volumen:c]
[Ausdehnung:c, Ausweitung:c, Dehnung:c, Erweiterung:c, ...]
[Geburt:c, Fehlgeburt, Frühgeburt]
[Geburt:c, Wiedergeburt]
[ Abweichung:c, Differenz:c, Abnormität, Anomalie, ...]
[ Abweichung:c, Differenz:c, Meinungsverschiedenheit]
[Probe:c, Blutprobe, Gesteinsprobe, Urinprobe, Wasserprobe]
[Bühnenprobe:c, Probe:c, Chorprobe, Generalprobe]
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Related Recent Work

Diana McCarthy, Rob Koeling, Julie Weeds, and John Carroll


Finding predominant senses in untagged text. In Proc. of ACL 2004.
Chan, Yee Seng and Ng, Hwee Tou (2005)

Word Sense Disambiguation with Distribution Estimation. Proc. of IJCAI
2005.

Mohammad, Saif and Hirst, Graeme.

Determining word sense dominance using a thesaurus. Proc. of EACL
2006.
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Day 2 - Introduction

Words and Object Descriptions

Semantics on the Semantic Web


The Lexical Semantic Web


Semantic Web, Ontologies and Natural Language Processing
Knowledge Representation as Word Meaning
A Lexicon Model for Ontologies

Enriching Ontologies with Linguistic Information
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Object Descriptions
Semantics on the Semantic Web
The “Lexical Semantic Web”
A Lexicon Model for Ontologies
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Web Consists of Non-Interpreted Data
Web
Text
Images
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Tables
DBs
Interpretation through Markup - Categories
Markup
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Web
Interpretation through Markup – User Tags
Markup
“Web 2.0”
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Interpretation through Markup – User Tags
Markup
“Web 2.0”
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Formal Interpretation - Knowledge Markup
Knowledge
Markup
Semantic Web
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies
Formal Interpretation - Knowledge Markup
Knowledge
Markup
Semantic Web
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies
Formal Interpretation - Knowledge Markup
Knowledge
Markup
Semantic Web
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies
Turns the Web into a Knowledge Base
Knowledge
Markup
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies
Enables Semantic Web Services …
Semantic
Web Services
Knowledge
Markup
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies
… and Intelligent Man-Machine Interface
Semantic
Web Services
Knowledge
Markup
Ontologies
Intelligent
Man-Machine Interface
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Semantic Web Layer cake
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Resource Description Framework (RDF)
DFKI GmbH
node1
www
http://www.dfki.de
Kaiserslautern
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
RDF : XML-based Representation
<?xml version=‘1.0’ ?>
<rdf:RDF
xmlns:rdf=“… rdf-syntax-ns#”
xmlns:rdfs=“… rdf-schema#”
xmlns=“http://example.org”>
<rdf:Description rdf:nodeID=“node1”>
<name>DFKI GmbH</name>
<location>Kaiserslautern</location>
<www rdf:resource=“http://www.dfki.de” />
</rdf:Description>
</rdf:RDF>
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
RDF Schema (RDFS)
Representation of classes and properties
Student
Person
is-a
Course
Teacher
rdf:Literal
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
RDFS : XML-based Representation
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Web Ontology Language (OWL)

OWL adds further modelling vocabulary on top of RDFS, e.g.



Class equivalence
Property types (data vs. object property)
Based on Description Logics, three versions



OWL Lite
OWL DL
OWL Full
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
OWL
Extended knowledge representation
Student
disjoint
Person
is-a
Teacher
rdf:Literal
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Course
OWL : XML-based Representation
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
XML – RDF – RDFS - OWL
Syntax
XML
Semantics
XML Schema
Namespaces
Interpretation Context
Data Types
RDF Schema
Formalization:
Class Definition, Properties
OWL
Formalization:
extended Class Definition,
Properties, Property Types
RDF
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – What they are

Ontology refers to an engineering artifact



a specific vocabulary used to describe a certain reality
a set of explicit assumptions regarding the intended
meaning of the vocabulary
An Ontology is


an explicit specification of a conceptualization [Gruber 93]
a shared understanding of a domain of interest
[Uschold/Gruninger 96]
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – Why you need them

Make domain assumptions explicit



Easier to exchange domain assumptions
Easier to understand and update legacy data
Separate domain knowledge from operational knowledge

Re-use domain and operational knowledge separately

A community reference for applications

Shared understanding of what particular information
means
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Applications of Ontologies

NLP





Information Extraction, e.g. Buitelaar et al. 06, Mädche, Staab &
Neumann 00, Nedellec, Rebholz
Information Retrieval (Semantic Search), e.g. WebKB (Martin et al.
00), OntoSeek (Guarino et al. 99), Ontobroker (Decker et al. 99)
Question Answering, e.g. Harabagiu, Schlobach & de Rijke, Aqualog
(Lopez and Motta 04)
Machine Translation, e.g. Nirenburg et al. 04, Beale et al. 95, Hovy,
Knight
Other






Business Process Modeling, e.g. Uschold et al. 98
Digital Libraries, e.g. Amann & Fundulaki 99
Information Integration, e.g. Kashyap 99; Wiederhold 92
Knowledge Management (incl. Semantic Web), e.g. Fensel 01, Staab
& Schnurr 00; Sure et al. 00, Abecker et al. 97
Software Agents, e.g. Gluschko et al. 99; Smith & Poulter 99
User Interfaces, e.g. Kesseler 96
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies and Their Relatives
Catalogs
Thesauri
Glossaries &
Terminologies
Formal isa
Semantic Networks
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
General logical
constraints
Formal Instance
Axioms:
Disjoint/Inverse…
Thesauri – Examples : EuroVoc

EuroVoc


covers terminology in all of the official EU languages
for all fields (27) that concern the EU institutions, e.g. politics,
trade, law, science, energy, agriculture
MT
UF
BT1
BT2
NT1
NT1
RT
3606 natural and applied sciences
gene pool
genetic resource
genetic stock
genotype
heredity
biology
life sciences
DNA
eugenics
genetic engineering (6411)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Thesauri – Examples : MeSH

MeSH (Medical Subject Headings)
 organized by terms (~ 250,000) that correspond to medical subjects
 for each term syntactic, morphological or semantic variants are given
MeSH Heading
Entry Term
Entry Term
Entry Term
Entry Term
Entry Term
Entry Term
Entry Term
Entry Term
See Also
Databases, Genetic
Genetic Databases
Genetic Sequence Databases
OMIM
Online Mendelian Inheritance in Man
Genetic Data Banks
Genetic Data Bases
Genetic Databanks
Genetic Information Databases
Genetic Screening
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Semantic Networks - Examples : UMLS

Unified Medical Language System


integrates linguistic, terminological and semantic information
Semantic Network consists of 134 semantic types and 54
relations between types
Pharmacologic Substance
Pharmacologic Substance
Pharmacologic Substance
Pharmacologic Substance
Pharmacologic Substance
Pharmacologic Substance
affects
causes
complicates
diagnoses
prevents
treats
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Pathologic Function
Pathologic Function
Pathologic Function
Pathologic Function
Pathologic Function
Pathologic Function
Semantic Networks - Examples : GO

GO (Gene Ontology)
 Aligns descriptions of gene products in different databases,
including plant, animal and microbial genomes
 Organizing principles are molecular function, biological process
and cellular component
Accession:
Ontology:
Synonyms:
Definition:
Term Lineage
GO:0009292
biological process
broad: genetic exchange
In the absence of a sexual life cycle, the processes
involved in the introduction of genetic information to create
a genetically different individual.
all : all (164142)
GO:0008150 : biological process (115947)
GO:0007275 : development (11892)
GO:0009292 : genetic transfer (69)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – Example I
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – Example II
Geographical Entity (GE)
is-a
flow_through
Inhabited GE
Natural GE
capital_of
mountain
river
instance_of
located_in
Zugspitze
height (m)
2962
city
country
Neckar
length (km)
367
F-Logic
Ontology
capital_of
Germany
flow_through
located_in
flow_through
Stuttgart
similar
Berlin
Design: Philipp Cimiano
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies for NLP

Information Retrieval


Machine Translation


Interlingua
Information Extraction



Query Expansion
Template Definition
Semantic Integration
Question Answering


Question Analysis
Answer Selection
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Information Extraction

Class-based Template Definition


Allows for Reasoning over Extracted Templates with
Respect to the Ontology (see e.g. [Nedellec and
Nazarenko 2005] for discussion)
Semantic Integration


Extraction from Heterogeneous Sources (Text, Tables
and other Semi-Structured Data, Image Captions) –
SmartWeb [Buitelaar et al. 06]
Multi-Document Information Extraction – ArtEquAKT
[Alani et al. 2003]
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Question Answering

Question Analysis


Ontology/WordNet-based Semantic Question Interpretation (e.g.
[Pasca and Harabagiu 01])
Answer Selection

Ontology/WordNet-based Reasoning for Answer Type-Checking




Ontology of Events [Sinha and Narayanan 05]
Geographical Ontology, WordNet [Schlobach & de Rijke 04]
WordNet [Pasca and Harabagiu 01]
Ontology-based Question Answering

Derive Answers from a Knowledge Base (e.g. Aqualog [Lopez &
Motta 04])
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontology Life Cycle
Populate
Knowledge Base Generation
Validate
Consistency Checks
Create/Select
Development and/or Selection
Evolve
Extension, Modification
Deploy
Knowledge Retrieval
Maintain
Usability Tests
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
NLP in the Ontology Life Cycle
Ontology Population
Information Extraction
KB Retrieval
Ontology Learning
Question Answering
Text Mining
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontology Learning
x ( country(x)  y capital_of (y, x)  z ( capital_of (z, x)  y  z))
disjoint(r iver, mountain)
GeneralAxioms
Axiom Schemata
capital_of R located_in
Relation Hierarchy
flow_throu gh(dom : river, range : GE)
Relations
capital C city, city C Inhabited GE
Concept Hierarchy
c : country : i(c), c , Ref C (c)
Concept Formation
{country, nation, Land}
river, country, nation, city, capital,.. .
(Multilingual) Synonyms
Terms
Design: Philipp Cimiano
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Object Descriptions
Semantics on the Semantic Web
The “Lexical Semantic Web”
A Lexicon Model for Ontologies
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Dictionary: Words and Senses

Represent interpretations of words through senses,
very much like classes that are assigned to a word, e.g.
article
1. An individual thing or element of a class…
2. A particular section or item of a series in a written document…
3. A non-fictional literary composition that forms an independent
part of a publication…
4. The part of speech used to indicate nouns and to specify their
application
5. A particular part or subject; a specific matter or point
(as provided by http://dictionary.reference.com/)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontology: Classes and Labels - I

Ontologies assign labels (i.e. words) to a given class

In the COMMA ontology on document management the class
article corresponds to sense 2 (‘section of a written document’):
http://pauillac.inria.fr/cdrom/ftp/ocomma/comma.rdfs
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontology Classes and Labels - II

In the GOLD ontology on linguistics, the class label
article corresponds to sense 4 (‘part of speech ’):
http://emeld.org/gold
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
The Meaning of Director - I
The Semantic Web can be viewed as a large, distributed
dictionary (or rather a semantic lexicon) in which we can
look up the meaning of words, e.g. director
… as a ‘role’ (AgentCities ontology)
http://www-agentcities.doc.ic.ac.uk/ontology/shows.daml
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
The Meaning of Director - II
… as ‘head of a program’ (University Benchmark ontology)
http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Exploring the Lexical Semantic Web

Collect ontologies

OntoSelect

Analyse the use of class/property labels

Treat class/property labels as lexical entries


Normalize
Organize by language
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontology Collection

OntoSelect


Web Monitor on DAML, RDFS, OWL Files
Download, Analyze and Store Included Information
and Metadata




Class and Property Labels
Multilingual Information
Included Ontologies
Ontology Ranking and Selection Functionalities
http://olp.dfki.de/OntoSelect
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
OntoSelect
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Multilinguality on the Semantic Web
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Multilingual Labels
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
“Lexical Semantic Ambiguity”
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Words and Object Descriptions
Semantics on the Semantic Web
The “Lexical Semantic Web”
A Lexicon Model for Ontologies
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – Example III
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Ontologies – Example III (continued)
Student
studies_at
located_at
University
Campus
works_at
“Fakultät”
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
is_part_of
Staff
Ontologies – Example III (continued)
Student
studies_at
located_at
University
Campus
works_at
“Fakultät”
is_part_of
has_German_term
Fakultät
has_Dutch_term
has_US_English_term
School
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Faculteit
Staff
Ontologies – Example III (continued)
University
“Fakultät”
is_part_of
has_term
Term
instance_of
instance_of
Fakultät
language
DE
faculteit
language
NL
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
school
language
EN-US
Semiotic Triangle



Ogden & Richards, 1923
based on Structural Linguistics studies (de Saussure, 1916)
adopted in Knowledge Representation (e.g. Sowa, 1984)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Legend
LingInfo Model – Simplified
rdf:type
URI
rdfs:subClassOf
property ...
feat:ClassWithFeats
feat:ClassWithFeats
rdfs:
subClassOf
o:Defender
feat:lingFeat
...
classes
rdfs:Class
o:FootballPlayer
feat:ClassWithFeats
meta-classes
rdfs:Class
if:ImgFeat
feat:ClassWithFeats
o:Midfielder
rdfs:Class
...
lf:LingFeat
feat:imgFeat
feat:lingFeat
lf:LingFeat
lf:LingFeat
lf:lang “de”
lf:term “Abwehrspieler”
…
lf:lang “de”
lf:term “Mittelfeldspieler”
…
if:ImgFeat
instances
if:color “#111111”
lf:texture “&keypatchSet_223
…
Design: Michael Sintek
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
LingInfo Model
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
LingInfo Instances - Example
inst0 : LingInfo
lang
de
morphSynDecomp
term
Fußballspielers
Fußballspielers
„of the football player“
inst2 : Stem
case
nominative
gender
male
number
singular
ortographicForm Fußballspieler
partOfSpeech
Noun
isComposedOf
…
inst3 : Stem
analysisIndex
1
orthographicForm Fußball
...
isComposedOf
function
modifier
root
semantics
inst1 : InflectedWordForm
case
genitive
gender
male
number
singular
ortographicForm Fußballspielers
partOfSpeech
Noun
wordForm
…
inst1 : Root
orthographicForm Spieler
…
inst8 : Stem
analysisIndex
2
orthographicForm Spieler
…
root
…
o:BallObject
inst7 : Stem (Ball)
inst4 : Root (Ball)
inst5 : Stem (Fuß)
inst6 : Root (Fuß)
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
LingInfo Predicate-Arg Structure
Design: Anette Frank
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Conclusions
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia
Conclusions

WordNet: Appropriate Use may include



Introduction of underspecified senses (sense
grouping)
Tuning to a domain
The “Lexical Semantic Web”



The Semantic Web (and Web 2.0) is a potentially
rich resource for (formal) lexical semantics
Mining such resources for lexical semantics (i.e.
compilation of a distributed semantic lexicon) only
just started
Ontologies to be extended with linguistic/lexical
information
© Paul Buitelaar: Lexical Semantics and Ontologies
Tutorial at ACL/HCSnet, July 2006, Melbourne, Australia